Computing systems can be used to understand user behavior. For example, a computing system can predict whether a user will perform a desired outcome, such as whether a user conversion will occur when the user is exposed to targeted content. With the results of the prediction, an operator can configure a computing system to send relevant targeted content to user devices.
In particular, by analyzing video interactions, augmented reality systems can provide a better understanding of user behavior, leading to content with higher relevance and therefore a more efficient use of computing resources. Augmented reality systems capture video of a person or a scene, and play an augmented scene back in real time. For example, an augmented reality system captures video of an individual, creates a three-dimensional model of the individual's face, and superimposes additional items such as a pair of sunglasses or a hat.
Existing solutions for augmented reality are inadequate for generating input usable by computing systems to direct future targeted content. Such solutions are unable to reliably visualize an item in real time, unable to predict user behavior, or unable to provide recommendations to a user. Accordingly, solutions are needed in order for real-time augmented reality systems to better understand user behavior and provide improved targeted content that increases the efficiency of computing resources.